Data Mining Research

Given the availability of large-scale source-code repositories, there have been a large number of applications for clone detection. Unfortunately, despite a decade of active research, there is a marked lack in clone detectors that scale to large software repositories. In particular for detecting near-miss clones where significant editing activities may take place in the cloned code.

We developed a token-based approach for large scale code clone detection which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also developed a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrated how it can increase the retrieval speed and decrease the memory usage of the index-based approaches.

Sourcerer is an ongoing research project at the University of California, Irvine aimed at exploring open source projects through the use of code analysis. The existence of an extremely large body of open source code presents a tremendous opportunity for software engineering research. Not only do we leverage this code for our own research, but we provide the open source Sourcerer Infrastructure and curated datasets for other researchers to use.